{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matplotlib 实际上是一种比较低级的工具\n",
    "# 绘制图表，组装一些基本组件即可：\n",
    "#    数据展示（图表类型：线型图、柱状图、盒形图、散布图、等值线图等等）\n",
    "#    图例、标题、刻度标签、其他注解型信息   \n",
    "\n",
    "# pandas 本身有内置绘图方法，用于简化从 DataFrame 和 Series 数据中绘制图像\n",
    "# seaborn 简化了许多常见的可视类型的创建（https://seaborn.pydata.org/），静态图形库\n",
    "\n",
    "# 提示： 引入 seaborn 会修改 matplotlib 默认的颜色方案和绘图类型，以提高可读性和美观度\n",
    "# 即使不使用 seaborn API，可能也会引入 seaborn，作为提高美观度和绘制常见 matplotlib 图形的简化方法\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8b01b48>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 线型图\n",
    "# Series 和 DataFrame 都有一个用于生成各类图表的 plot 方法。 \n",
    "#   默认情况下， 它们所生成的是线型图\n",
    "s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))\n",
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 该 Series 对象的索引会被传给 matplotlib， 并用以绘制 X轴 \n",
    "# 可以通过 use_index=False 禁用该功能\n",
    "# X轴的刻度和界限可以通过 xticks 和 xlim 选项进行调节， \n",
    "# Y轴就用 yticks 和 ylim\n",
    "\n",
    "# plot 参数的完整列表\n",
    "# 参数        说明\n",
    "# label       用于图例的标签\n",
    "# ax          要在其上进行绘制的 matplotlib subplot对象。如果没有设置，则使用当前 matplotlib subplot\n",
    "# style       将要传给 matplotib 的风格字符串( 如\"ko--')\n",
    "# alpha       图表的填充不透明度(0到1之间 )\n",
    "# kind        可以是'line'、'bar'. 'barh'. 'kde'\n",
    "# logy        在Y轴上使用对数标尺\n",
    "# use_ index  将对 象的索引用作刻度标签\n",
    "# rot         旋转刻度标签(0到360)\n",
    "# xticks      用作x轴刻度的值\n",
    "# yticks      用作Y轴刻度的值\n",
    "# xlim        X轴的界限(例如[0, 10])\n",
    "# ylim        Y轴的界限\n",
    "# grid        显示轴网格线(默认打开)\n",
    "\n",
    "# pandas 的大部分绘图方法都有一个可选的 ax 参数， \n",
    "#  可以是一个 matplotlib 的 subplot对象, 这使能够在网格布局中更为灵活地处理 subplot 的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8dfe188>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DataFrame 的 plot 方法会在一个 subplot 中为各列绘制一条线，并自动创建图例\n",
    "df = pd.DataFrame(np.random.randn(10, 4).cumsum(0),\n",
    "                  columns=['A', 'B', 'C', 'D'],\n",
    "                  index=np.arange(0, 100, 10))\n",
    "\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot 属性包含一批不同绘图类型的方法\n",
    "#    df.plot()  ==  df.plot.line()\n",
    "\n",
    "# pandas 的绘图是基于 matplotlib\n",
    "# 笔记： plot 的其他关键字参数会被传给相应的 matplotlib 绘图函数\n",
    "# 所以要更深入地自定义图表， 就必须学习更多有关 matplotlib API 的知识\n",
    "\n",
    "# DataFrame 还有一些用于对列进行灵活处理的选项\n",
    "# 参数              说明\n",
    "# subplots          将各个DataFrame列绘制到单独的subplot中\n",
    "# sharex            如果subplots=True，则共用同- -个X轴，包括刻度和界限\n",
    "# sharey            如果subplots=True，则共用同- -个Y轴\n",
    "# figsize           表示图像大小的元组\n",
    "# title             表示图像标题的字符串\n",
    "# legend            添加一个subplot图例(默认为True)\n",
    "# sort_ .columns    以字母表顺序绘制各列，默认使用当前列顺序\n",
    "\n",
    "# 有关时间序列的绘图，需要更加复杂的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9078708>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 柱状图\n",
    "\n",
    "# plot.bar() 和 plot.hbar() 分别绘制 水平 和 垂直 的柱状图\n",
    "# Series 和 DataFrame 的索引将会被用于 X（bar） 或者 Y（barh）刻度\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "\n",
    "data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n",
    "\n",
    "data.plot.bar(ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot.barh(ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Genus</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.689379</td>\n",
       "      <td>0.125919</td>\n",
       "      <td>0.907432</td>\n",
       "      <td>0.069281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.651969</td>\n",
       "      <td>0.021599</td>\n",
       "      <td>0.420009</td>\n",
       "      <td>0.529450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.252793</td>\n",
       "      <td>0.207169</td>\n",
       "      <td>0.176922</td>\n",
       "      <td>0.628419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>0.182816</td>\n",
       "      <td>0.854052</td>\n",
       "      <td>0.733365</td>\n",
       "      <td>0.256557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>0.319337</td>\n",
       "      <td>0.742839</td>\n",
       "      <td>0.383368</td>\n",
       "      <td>0.676732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>0.172348</td>\n",
       "      <td>0.426087</td>\n",
       "      <td>0.286059</td>\n",
       "      <td>0.372377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Genus         A         B         C         D\n",
       "one    0.689379  0.125919  0.907432  0.069281\n",
       "two    0.651969  0.021599  0.420009  0.529450\n",
       "three  0.252793  0.207169  0.176922  0.628419\n",
       "four   0.182816  0.854052  0.733365  0.256557\n",
       "five   0.319337  0.742839  0.383368  0.676732\n",
       "six    0.172348  0.426087  0.286059  0.372377"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# color='k' 和 alpha=0.7 设定了图形的颜色为黑色， 并使用部分的填充透明度\n",
    "\n",
    "# 对于 DataFrame，柱状图会将每一行的值分为一组，并排显示\n",
    "df = pd.DataFrame(np.random.rand(6, 4),\n",
    "                  index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
    "                  columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x91956c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEJCAYAAACE39xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAW50lEQVR4nO3df5RfdZ3f8efbSUJYy1IMYbcYYAYKhyQEAsQoBIWyKKBdfijHE0BJdxXEithiqdG1GNmeFqhV90iaEn5USmuyKRZMlYoeAkFY0CQQISEBYwgyopCkbgRJNIR3//h+kx2Gmcw3mTvfm/nk+Tgn58y9nzv3+74nmdd88rmfz72RmUiShr+31F2AJKkaBrokFcJAl6RCGOiSVAgDXZIKYaBLUiFG1PXBBx54YHZ2dtb18ZI0LC1btmxDZo7tq622QO/s7GTp0qV1fbwkDUsR8Vx/bQ65SFIhDHRJKoSBLkmFqG0MXZLabevWrXR3d7Nly5a6SxnQ6NGjGTduHCNHjmz5ewx0SXuN7u5u9ttvPzo7O4mIusvpV2ayceNGuru76erqavn7HHKRtNfYsmULY8aM2aPDHCAiGDNmzC7/T8JAl7RX2dPDfLvdqdNAl6SmF198kYsuuojDDz+cE088kZNOOom77rqr7rJatteMoU+6fVK/bU/OeLKNlUjaE2Um5513HjNmzOBb3/oWAM899xwLFy6subLW2UOXJGDRokWMGjWKyy+/fMe+ww47jE9/+tNs27aNq6++mne84x0ce+yx3HTTTQA88MADnHbaaVxwwQUcffTRXHzxxWx/C1xnZycbNmwAYOnSpZx22mkALF68mMmTJzN58mSOP/54Xn755cquYa/poUvSzqxcuZITTjihz7Zbb72V/fffnyVLlvD73/+eadOm8b73vQ+Axx9/nJUrV3LwwQczbdo0Hn74YU455ZR+P+crX/kKs2fPZtq0abzyyiuMHj26smsw0CWpD5/61Kd46KGHGDVqFIcddhhPPPEEd955JwCbNm3iZz/7GaNGjWLq1KmMGzcOgMmTJ7Nu3bqdBvq0adO46qqruPjii/ngBz+443ur4JCLJAETJ07kscce27E9e/Zs7rvvPtavX09m8o1vfIPly5ezfPlynn322R099H322WfH93R0dPDaa68BMGLECF5//XWAN0w/nDlzJrfccgubN2/mXe96F6tXr67sGgx0SQJOP/10tmzZwpw5c3bse/XVVwE488wzmTNnDlu3bgXgmWee4Xe/+91Oz9fZ2cmyZcsA+Pa3v71j/89//nMmTZrE5z73OaZMmWKgS1LVIoK7776bxYsX09XVxdSpU5kxYwbXX389H//4x5kwYQInnHACxxxzDJ/4xCd29MT786UvfYnPfOYzvPvd76ajo2PH/q9//escc8wxHHfccey7776cffbZ1V3D9juy7TZlypRs5/PQnbYoadWqVYwfP77uMlrWV70RsSwzp/R1vD10SSqEgS5JhTDQJakQBrokFcKFRVI7zNp/J22b2leHimYPXZIKYaBLUpvdddddRESli4rAIRdJe7HOmd+r9HzrrvtAS8fNmzePU045hfnz5zNr1qzKPt8euiS10SuvvMLDDz/Mrbfeyvz58ys9t4EuSW109913c9ZZZ3HUUUfxtre97Q0PBBssA12S2mjevHlMnz4dgOnTpzNv3rzKzu0YuiS1ycaNG1m0aBErVqwgIti2bRsRwQ033FDJy6vtoUtSm9x5551ccsklPPfcc6xbt47nn3+erq4uHnrooUrO31KgR8RZEfF0RKyJiJl9tB8aEfdHxOMR8UREvL+S6iSpIPPmzeP8889/w74PfehDO15KPVgDDrlERAcwG3gv0A0siYiFmflUj8O+CCzIzDkRMQG4B+ispEJJGiKtTjOsygMPPPCmfVdeeWVl52+lhz4VWJOZazPzD8B84NxexyTwx82v9wdeqKxCSVJLWrkp+nbg+R7b3cA7ex0zC/hBRHwaeCtwRiXVSZJa1koPva9br71fc3Qh8M3MHAe8H7gjIt507oi4LCKWRsTS9evX73q1kqR+tRLo3cAhPbbH8eYhlY8BCwAy8xFgNHBg7xNl5tzMnJKZU8aOHbt7FUuS+tRKoC8BjoyIrogYBUwHFvY65hfAnwFExHgagW4XXJLaaMBAz8zXgCuAe4FVNGazrIyIayPinOZhnwUujYifAvOAf5F1vX1akvZSLa0Uzcx7aExF7Lnvmh5fPwVMq7Y0SSpPR0cHkyZNIjPp6Ojgxhtv5OSTT67k3C79l7T32tmbpHbrfAO/fWrfffdl+fLlANx77718/vOfZ/HixZV8vEv/Jakmv/3tbznggAMqO589dElqo82bNzN58mS2bNnCr371KxYtWlTZuQ10SWqjnkMujzzyCJdccsmOpy8OlkMuklSTk046iQ0bNlDVQksDXZJqsnr1arZt28aYMWMqOZ9DLpLURtvH0AEyk9tvv52Ojo5Kzm2gS9p7tTDNsGrbtm0bsnMb6JIGb2fzuWsIzb2VY+iSVAh76FLNJt0+qd+2J2c82cZKNNzZQ5ekQhjoklQIA12SCmGgS1Kb/frXv2b69OkcccQRTJgwgfe///0888wzgz6vN0Ul7bV2dkN6d7RyEzszOf/885kxYwbz588HYPny5bz44oscddRRg/p8A12S2uj+++9n5MiRXH755Tv2bV85OlgOuUhSG61YsYITTzxxSM5toEtSIQx0SWqjiRMnsmzZsiE597AbQ++c+b1+29Zd94E2ViJJu+7000/nC1/4AjfffDOXXnopAEuWLOHVV1/l1FNPHdS57aFLUhtFBHfddRc//OEPOeKII5g4cSKzZs3i4IMPHvS5h10PXZKqUtezcg4++GAWLFhQ+XkNdEm1WXX0+H7bxq9e1cZKyuCQiyQVwkCXpEIY6JJUCANdkgrhTdE9jPPsJe0ue+iS1EYdHR1MnjyZiRMnctxxx/HVr36V119/vZJz20OXtNfa2bTJ3dHKVMt9992X5cuXA/DSSy9x0UUXsWnTJr785S8P+vPtoUtSTQ466CDmzp3LjTfeSGYO+nwGuiTV6PDDD+f111/npZdeGvS5DHRJqlkVvXNwDF0FcPm4hrO1a9fS0dHBQQcdNOhz2UOXpJqsX7+eyy+/nCuuuIKIGPT57KFLUhtt3ryZyZMns3XrVkaMGMFHP/pRrrrqqkrO3VKgR8RZwN8AHcAtmXldH8d8GJgFJPDTzLyokgolaYjUMSS3bdu2ITv3gIEeER3AbOC9QDewJCIWZuZTPY45Evg8MC0zfxMRgx8MkiTtklbG0KcCazJzbWb+AZgPnNvrmEuB2Zn5G4DMHPz8G0nSLmkl0N8OPN9ju7u5r6ejgKMi4uGIeLQ5RPMmEXFZRCyNiKXr16/fvYolSX1qJdD7uvXae9LkCOBI4DTgQuCWiPjHb/qmzLmZOSUzp4wdO3ZXa5WkQatqzvdQ2506Wwn0buCQHtvjgBf6OOY7mbk1M58FnqYR8JK0xxg9ejQbN27c40M9M9m4cSOjR4/epe9rZZbLEuDIiOgCfglMB3rPYLmbRs/8mxFxII0hmLW7VIkkDbFx48bR3d3NcBjyHT16NOPGjdul7xkw0DPztYi4AriXxrTF2zJzZURcCyzNzIXNtvdFxFPANuDqzNy4y1cgSUNo5MiRdHV11V3GkGlpHnpm3gPc02vfNT2+TuCq5h9JUg1c+i9JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEL5TdC+w6ujx/bbV8QouSUPDHrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhRhRdwGStMebtf9O2ja1r44BGOiFmHT7pH7bFrSxDkn1aWnIJSLOioinI2JNRMzcyXEXRERGxJTqSpQktWLAQI+IDmA2cDYwAbgwIib0cdx+wJXAj6suUpI0sFZ66FOBNZm5NjP/AMwHzu3juL8GbgC2VFifJKlFrQT624Hne2x3N/ftEBHHA4dk5ncrrE2StAtaCfToY1/uaIx4C/A14LMDnijisohYGhFL169f33qVkqQBtRLo3cAhPbbHAS/02N4POAZ4ICLWAe8CFvZ1YzQz52bmlMycMnbs2N2vWpL0Jq0E+hLgyIjoiohRwHRg4fbGzNyUmQdmZmdmdgKPAudk5tIhqViS1KcBAz0zXwOuAO4FVgELMnNlRFwbEecMdYGSpNa0tLAoM+8B7um175p+jj1t8GVJKoWL3trHZ7lIUiFc+i9JQ2TV0eP7bRu/elXln2cPXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoTPcpHUks6Z3+u3bd3oNhaiftlDl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEM5Dl6RBmHT7pH7bFrSxDrCHLknFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgrhtEW11U4fwXrdB9pYyfCw6ujx/baNX72qjZVoOLCHLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSpES4EeEWdFxNMRsSYiZvbRflVEPBURT0TEfRFxWPWlSpJ2ZsBAj4gOYDZwNjABuDAiJvQ67HFgSmYeC9wJ3FB1oZKknWtl6f9UYE1mrgWIiPnAucBT2w/IzPt7HP8o8JEqi9ReYtb+O2nb1L46pGGqlSGXtwPP99jubu7rz8eA/zuYoiRJu66VHnr0sS/7PDDiI8AU4NR+2i8DLgM49NBDWyxRktSKVnro3cAhPbbHAS/0PigizgD+CjgnM3/f14kyc25mTsnMKWPHjt2deiVJ/Wgl0JcAR0ZEV0SMAqYDC3seEBHHAzfRCPOXqi9TkjSQAYdcMvO1iLgCuBfoAG7LzJURcS2wNDMXAv8J+EfA/4oIgF9k5jlDWLe0x9nps95Ht7EQ7bVaesFFZt4D3NNr3zU9vj6j4rokSbvINxZpWJh0+6R+2xa0sQ5pT2agSxJlDJn5LBdJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSpEWStFd/bGmy6fvy6pbPbQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklSIsh7OtZtWHT2+37bxq1e1sRJJ2n320CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwqX/w8ms/ftv6zq0fXVI2iPZQ5ekQrQU6BFxVkQ8HRFrImJmH+37RMTfNtt/HBGdVRcqSdq5AQM9IjqA2cDZwATgwoiY0OuwjwG/ycx/CnwNuL7qQiVJO9dKD30qsCYz12bmH4D5wLm9jjkXuL359Z3An0VEVFemJGkgrdwUfTvwfI/tbuCd/R2Tma9FxCZgDLCh50ERcRlwWXPzlYh4eneK7s/Of4OsOLB3Pdv1/u/GG0+65/xe8vqG7/WVfG3g9dHe6zusv4ZWAr2vT83dOIbMnAvMbeEzKxcRSzNzSh2f3Q5e3/BV8rWB19dOrQy5dAOH9NgeB7zQ3zERMQLYH/h/VRQoSWpNK4G+BDgyIroiYhQwHVjY65iFwIzm1xcAizLzTT10SdLQGXDIpTkmfgVwL9AB3JaZKyPiWmBpZi4EbgXuiIg1NHrm04ey6N1Uy1BPG3l9w1fJ1wZeX9uEHWlJKoMrRSWpEAa6JBXCQJekQhjo2qNFxFvrrqFq0XDIwEdKu6bYQI+IoyLivohY0dw+NiK+WHddVYqI4yLiiuaf4+qup0oRcXJEPAWsam4fFxH/peayKtGc0nt33XUMlb3kZ++MPvbN6OvYdio20IGbgc8DWwEy8wn2zOmUuyUiPgP8T+Cg5p//ERGfrreqSn0NOBPYCJCZPwXeU2tF1Xo0It5RdxFDpOifvaZrImJORLw1Iv4kIv4P8Od1F1XyCy7+KDN/0usZYa/VVcwQ+Bjwzsz8HUBEXA88Anyj1qoqlJnP9/r721ZXLUPgnwGfiIjngN/ReHxGZuax9ZZVidJ/9gBOBT4LLG9uX5OZ82qsByg70DdExBE0nykTERcAv6q3pEoFbwy4bQz0DKHh5fmIOBnI5grlK2kOvxTi7LoLGEKl/+wBHEDjIYU/p/E4lMMiIupeIV9yoH+KxgquoyPil8CzwEfqLalS/w34cUTc1dw+j8aK3VJcDvwNjSd5dgM/oPF3WoqSV/T19bN3cb0lVe5R4LrMvC0i9qXxDoiHgZPrLKr4laLNWRJvycyX666lahFxAnAKjZ75g5n5eM0lqUUR8SSNUA9gNNAFPJ2ZE2strAIR0ZGZ2wr/2Ts0M3/Ra997MvPBumqCgnvoEbEP8CGgExixfTwvM6+tsazKNJ+l8yPg1u3j6CWJiKOAOcCfZOYxEXEscE5m/vuaS6tEZk7qud385fyJmsqp2rMR8X3gb4FFdRdTpYg4OjNXAwdGxIG9ml+po6aeiu2hN/9BbQKW0WOsOTP/c21FVSgi/pJG7/wk4GUa4f5gZn6n1sIqEhGLgauBmzLz+Oa+FZl5TL2VDZ2IeCwzT6i7jsFqDkH8OY2ZLScA3wXmZ+ZDtRZWgYiYm5mXRcT9PXbvCNHMPL2GsnYoOdCL/uHfLiL+FPgw8G+AAzJzv5pLqkRELMnMd0TE4z0CfXlmTq67tipExFU9Nt9CI/jGZOaZNZU0JCLiABr3Qi7OzI6666lKRHwY+H5m/jYi/h2Nv7+/zszH6qyr5HnofxcRkwY+bHiKiFsi4u9oDEuMoPEc+gPqrapSpc+U2K/Hn32A7/Hmd/UOWxFxanMh2GM07hF8uOaSqvbFZpifArwX+CaNn8VaFTuGTmM44i8iYi3we8qa5wuNd7Z2AH9P4xn0GzKzpLm+Rc+UyMwvA0TEfo3NrH38tSoR8SyN+dkLgKtLvMfDPwzjfgD4r5n5nYiYVWM9QNlDLofR6LG+u7nrQeDvM/O5+qqqXkSMp7Gi8l8DHZk5ruaSBi0i3gJckJkLSp0pERHHAHcAb2vu2gDMyMwV9VVVjYj448z8bd11DKWI+C7wS+AM4ERgM/CTzKz1ERwlB/pngI8D/5tG7/w84ObMLGIlZUT8cxq/rN5D4xfXI8CPMvO2WgurSEQ8mJklLfV/g+Zw2V9l5v3N7dOA/5CZtc5jHoyI+LeZeUNEfIO+XxJ/ZQ1lDYmI+CPgLODJzPxZRPwTYFJm/qDWugoO9CeAk3osjX8r8EgpQy4RcRuN1wL+KDNfaO67PjM/V29l1WjeaNpMY+rbjv+yZ2YRLx+PiJ/27s31tW84iYiNmTkmIv4V8Jve7Zl5ew1l7VVKHkMvfWn85Mz8y177zgaKCHRg+7X1XB2awOE11DIU1jZ/ad3R3P4IjfsEw9mLzaHOv6DxrBq1WcmBXuTS+Ij4JPAvgcOb/wvZbj8aS4+LkJldddcwFCLijsz8KI11A538w5DgYhpBOJzNAb5P45fu0h77g7J+Ge+xih1ygTKXxkfE/jTGzP8jMLNH08ulDEds13w4Vyc9Oh6Z+d9rK6gCzWe8nw0spNGL3R52QBlDShExJzM/WXcde6OiA13DV0TcARxBY/rb9qGzHO431iLiSuCTNHqrv+zZROP67MVqtxno2iNFxCpgQt2PIx0q9mI1FEpeKarhbQXwp3UXMVQMcw2Fkm+KahhqvsoradzkfSoifkJjpS8AmXlOXbVJezoDXXuar9AYT76exsyk7bbvk9QPA117lMxcDBARI7d/vV3zsayS+mGga4+yt8yzl4aCs1y0R9mb5tlLVTPQJakQTluUpEIY6JJUCANdkgphoEtSIQx0SSrE/wcIwX/QAz/pEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9164a08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 注意， DataFrame 各列的名称 \"Genus\" 被用作了图例的标题\n",
    "\n",
    "# 设置 stacked=True 即可为 DataFrame 生成堆积柱状图， 这样每行的值就会被堆积在一起\n",
    "df.plot.barh(stacked=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size  1   2   3   4  5  6\n",
       "day                      \n",
       "Fri   1  16   1   1  0  0\n",
       "Sat   2  53  18  13  1  0\n",
       "Sun   0  39  15  18  3  1\n",
       "Thur  1  48   4   5  1  3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 笔记： 柱状图有一个非常不错的用法： \n",
    "#       利用 value_counts 图形化显示 Series 中各值的出现频率， 如 s.value_counts().plot.bar()\n",
    "\n",
    "# 有关小费的数据集为例， \n",
    "# 假设想要做一张堆积柱状图以展示每天各种聚会规模的数据点的百分比\n",
    "# 用 read_csv 将数据加载进来， 然后根据日期和聚会规模创建一张交叉表：\n",
    "\n",
    "dataset_path = './../dataset/'\n",
    "\n",
    "tips = pd.read_csv(dataset_path + 'tips.csv')\n",
    "\n",
    "party_counts = pd.crosstab(tips['day'], tips['size'])\n",
    "\n",
    "party_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.623529</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>0.011765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.086207</td>\n",
       "      <td>0.017241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size         2         3         4         5\n",
       "day                                         \n",
       "Fri   0.888889  0.055556  0.055556  0.000000\n",
       "Sat   0.623529  0.211765  0.152941  0.011765\n",
       "Sun   0.520000  0.200000  0.240000  0.040000\n",
       "Thur  0.827586  0.068966  0.086207  0.017241"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not many 1- and 6-person parties\n",
    "party_counts = party_counts.loc[:, 2:5]\n",
    "\n",
    "# normalize to sum to 1\n",
    "# 进行规格化，使得各行的和为 1，并生成图表\n",
    "party_pcts = party_counts.div(party_counts.sum(1), axis=0)\n",
    "party_pcts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbe7d8c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_pcts.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11 ms ± 13.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.063204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.162494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.172069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01     No  Sun  Dinner     2  0.063204\n",
       "1       10.34  1.66     No  Sun  Dinner     3  0.191244\n",
       "2       21.01  3.50     No  Sun  Dinner     3  0.199886\n",
       "3       23.68  3.31     No  Sun  Dinner     2  0.162494\n",
       "4       24.59  3.61     No  Sun  Dinner     4  0.172069"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 于是， 通过该数据集就可以看出， 聚会规模在周末会变大。\n",
    "\n",
    "# 对于在绘制一个图形之前，需要进行合计的数据， \n",
    "#    使用 seaborn 可以减少工作量, 用 seaborn 来看每天的小费比例\n",
    "\n",
    "# 约定 seaborn 简写 sns\n",
    "import seaborn as sns\n",
    "\n",
    "%timeit tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd1e6508>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JaquniPggcBlwZmbu3Jt1a9BJT0O9n5rcALwyCurefqr75EybJ3D2pXGC7HBePYFz7LhlLuA3T/J+p5o+lt88gfMog3HiupOe3vhKDzROam0GDhqGnpqWvZbXnrh+jMbJ0AOr6WHv6UBg/2p6HvAw4048DmpPNN4kHwGOHPf80O6nCXoa5v10ZNP0GcDaarpr73u1/hD28gf2EeChaidfVj13BY3fCABmAN+lcYLmx8ARTeteVq23Efj9unvptCfgbOCB6h/BfcAZdfeyFz29i8ZvOduBp4EHmtb9TNXrJuDTdffSaU/AScCGaj9tAD5bdy970dNtwC+B9dXX96bAfmrZ05Dvp69V7wXrgTtoCpFuve95WQ5JUtGwnJOQJNXAkJAkFRkSkqQiQ0KSVGRISJKKDAlJUpEhIbUhIuZGxPnV9KERcWMfXnNRRHyk168jTcSQkNozl8al28nMLZl5zh6W74ZFNP6YSqqNf0wntSEiXrlM80Yal204OjOPi4jzgI/x6uUP/ikzv1LYxijwQ+AeGpeIeAhYkpkvRMS7aPz17CxgJ/AhGn/9O5PGZVf+JjNX9qo/qcSRhNSeS4FHMnMRcMm4ee8G/pjGb/5/GBGLJ9jOAmB5Zi4EngPOj4jpwErgzzPzeOCDNC7xsYzGjXEWGRCqiyEhde7WzHw6M3cA/wz87gTLPpGZa6rpb1fLLgCeysx7oXEjmWzcP0SqnSEhdW78MduJjuG2Wjb2sI5UG0NCas82YE5h3oci4qCImEnjev5rCssBzI+IE6vpTwJ3AQ8Ch1bnJYiIOdX9QyZ6TakvDAmpDdm4ydOaiPgZcOW42XcB19O4XPNNmbl2gk39HDg3Iu6ncW+Gr2fj/sUfB66KiJ/SuG/0DBqXfj4mItZHxMe725HUHj/dJHWg+nTT4sz8fBvLjgI/yMzjelyW1DWOJCRJRY4kpC6LiIOB21vM+kB12EoaGoaEJKnIw02SpCJDQpJUZEhIkooMCUlS0f8DRI4OJnJ5kCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='tip_pct', y='day', data=tips, orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd24f1c8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn 的绘制函数使用 data参数（绘图的数据点）， 它可能是 pandas 的 DataFrame\n",
    "#      其它的参数是关于列的名字\n",
    "\n",
    "# 因为一天的每个值有多次观察， 柱状图的值是 tip_pct 的平均值\n",
    "# 绘制在柱状图上的黑线代表 95% 置信区间（可以通过可选参数配置） \n",
    "# seaborn.barplot 有颜色选项， 使能够通过一个额外的值设置\n",
    "sns.barplot(x='tip_pct', y='day', hue='time', data=tips, orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意， seaborn 已经自动修改了图形的美观度：默认调色板，图形背景和网格线的颜色\n",
    "# 可以用 seaborn.set 在不同的图形外观之间切换：\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd266548>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='tip_pct', y='day', hue='time', data=tips, orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd573d48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图 和 密度图\n",
    "\n",
    "# 直方图（histogram） 是一种可以对值频率进行离散化显示的柱状图\n",
    "# 数据点被拆分到离散的、间隔均匀的面元中，绘制的是各面元中数据点的数量\n",
    "# 以小费数据为例， 通过在 Series 使用 plot.hist 方法，可以生成一张“小费占消费总额百分比”的直方图\n",
    "tips['tip_pct'].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd613588>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 与此相关的一种图表类型是 密度图\n",
    "#  它是通过计算 “可能会产生观测数据的连续概率分布的估计” 而产生的\n",
    "# 一般的过程是将该分布近似为一组核（即诸如正态分布之类的较为简单的分布）\n",
    "# 因此，密度图 也被称作 KDE（Kernel Density Estimate，核密度估计）图\n",
    "# 使用 plot.kde 和 标准混合正态分布估计即可生成一张密度图\n",
    "tips['tip_pct'].plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd8c9548>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn 的 distplot 方法绘制 直方图 和 密度图 更加简单， \n",
    "#        还可以同时画出 直方图 和 连续密度估计图。\n",
    "\n",
    "\n",
    "# 作为例子，考虑一个双峰分布，由两个不同的标准正态分布组成\n",
    "comp1 = np.random.normal(0, 1, size=200)\n",
    "comp2 = np.random.normal(10, 2, size=200)\n",
    "values = pd.Series(np.concatenate([comp1, comp2]))\n",
    "sns.distplot(values, bins=100, color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.007904</td>\n",
       "      <td>0.045361</td>\n",
       "      <td>-0.396881</td>\n",
       "      <td>0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.021979</td>\n",
       "      <td>0.066753</td>\n",
       "      <td>-2.277267</td>\n",
       "      <td>0.139762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.002340</td>\n",
       "      <td>0.010286</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.160343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.037461</td>\n",
       "      <td>-0.200671</td>\n",
       "      <td>0.127339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>-0.405465</td>\n",
       "      <td>0.042560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cpi        m1  tbilrate     unemp\n",
       "198 -0.007904  0.045361 -0.396881  0.105361\n",
       "199 -0.021979  0.066753 -2.277267  0.139762\n",
       "200  0.002340  0.010286  0.606136  0.160343\n",
       "201  0.008419  0.037461 -0.200671  0.127339\n",
       "202  0.008894  0.012202 -0.405465  0.042560"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 散布图或点图\n",
    "\n",
    "# 点图或散布图是观察两个一维数据序列之间的关系的有效手段\n",
    "#  在下面这个例子中，加载了来自 statsmodels 项目的 macrodata 数据集，\n",
    "#                  选择了几个变量， 然后计算对数差\n",
    "\n",
    "macro = pd.read_csv(dataset_path + 'macrodata.csv')\n",
    "\n",
    "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]\n",
    "\n",
    "trans_data = np.log(data).diff().dropna()\n",
    "trans_data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Changes in log m1 versus log unemp')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FIpFIJN2GFCoSiUQi6TakUJFIJBJJtyGFikQikUi6DdmjXnJCUbqrnJIP9lJe3URhbhrFl4xg4pjCvh6WRDJgkJqK5IShdFc5z5dsp6beT4ZHp6bez/Ml2yndVd7XQ5NIBgxSqEhOGEo+2IuuK7idOopi/9R1hZIP9vb10CSSAYMUKpIThvLqJlyOxB48LodGRXVTH41IIhl4SKEiOWEozE0jGDYTtgXDJgW5aX00Iolk4CGFiuSEofiSERiGIBAyEML+aRiC4ktG9PXQJJIBgxQqkhOGiWMKuat4HDmZHhr8BjmZHu4qHiejvySSbkSGFEtOKCaOKZRCRCLpQfpEU1mzZg0zZsxg2rRpLF++vMX777//PjNnzuS6667jnnvuoa6uDoBDhw5xyy23cNVVV/HTn/6UxsbG3h66RCKRSNqh14VKeXk5Tz75JK+99hqrV6/mjTfeYO/eYyGdDQ0NLFy4kBdeeIH//u//ZtSoUTzzzDMALFq0iNmzZ7N27VrGjh3Lc88919vDl0gkEkk79LpQ2bx5M5MnTyY7O5u0tDSmT5/O2rVrY++Hw2EWLFhAYaFtohg1ahSHDx8mHA6zdetWpk+fDkBxcXHCcRKJRCLpe3pdqFRUVJCfnx97XVBQQHn5sYzmnJwcrrzySgACgQAvvPACV1xxBTU1NXi9XnTddgPl5+cnHCeRSCSSvqfXHfWWZaEoSuy1ECLhdRSfz8fPfvYzRo8ezQ033EB5eXmL/Vo7riPKyso6P+gUZ9u2bX09hB5Dzq1/IufWfzne+fW6UBk8eDClpaWx15WVlRQUFCTsU1FRwU9+8hMmT57M3LlzAcjNzcXn82GaJpqmtXpcMowdOxaXy3V8k0ghtm3bxoQJE/p6GD2CnFv/RM6t/9J8fsFgsNML8V43f02ZMoUtW7ZQXV2N3+9n/fr1TJ06Nfa+aZrcfffdXH311cybNy+mjTgcDiZOnMi7774LwOrVqxOOk0gkEknf0+uaSmFhIXPmzOHWW28lHA5z4403Mm7cOO68807uu+8+jhw5ws6dOzFNk3Xr1gG2dvHoo4+yYMECHnzwQZYtW8ZJJ53E0qVLe3v4EolEImmHPkl+LCoqoqioKGHbiy++CMDZZ5/N7t27Wz1u6NCh/Nd//VePj08ikUgkXUOWaZFIJBJJtyGFikQikUi6DSlUJBKJRNJtSKEikUgkkm5DChWJRCKRdBtSqEgkEomk25BCRSKRSCTdhhQqEolEIuk2pFCRSCQSSbchhYpEIpFIug3Zo14y4CjdVU7JB3spr26iMDeN4ktGyL70EkkvITUVyYCidFc5z5dsp6beT4ZHp6bez/Ml2yndJRu6SSS9gRQqkgFFyQd70XUFt1NHUeyfuq5Q8sHevh6aRHJCIM1fkgFFeXUTGZ7Er7XLoVFR3dTmMdJcJpF0H1JTkQwoCnPTCIbNhG3BsElBblqr+0tzmUTSvUihIhlQFF8yAsMQBEIGQtg/DUNQfMmIVveX5jKJpHuR5i9Jv6IjU9XEMYVQPI6SD/ZSUd1EQQfmrK6YyyQSSdv0iVBZs2YNy5YtwzAMbrvtNm655ZZW93vggQeYPHkyxcXFAKxatYonnniCvLw8AC655BLmzJnTa+OW9C1RU5WuKwmmKorHtRAsyfpECnPTqKn343Ye+1Noz1wmkUjap9eFSnl5OU8++SQlJSU4nU5mzZrFpEmTGDFiRMI+CxYsYMuWLUyePDm2vaysjAcffJBrr722t4ctSQHiTVUAbqdOAIOSD/Z22bFefMkIni/ZTgADl0MjGDbbNZdJJJL26XWhsnnzZiZPnkx2djYA06dPZ+3atdx7772xfdasWcPll18e2yfKjh072LdvH88//zyjRo1i/vz5ZGVl9er4JX1HT5iqOmsuSwVktJoklel1oVJRUUF+fn7sdUFBAdu3b0/Y54477gBg27ZtCdvz8/P58Y9/zPjx41m6dCmLFy/miSee6PlBS1KCnjJVdcZc1tckawKUSPqKXhcqlmWhKErstRAi4XV7PPvss7Hf77jjDq688spOX7+srKzTx6Q6zYXvQCJ+bmefLPjj1gD+ADg0hbApsEyYeqa7X96Droz5lQ2VhA0DBZUmw94WNixeWfMZSlN++wf3Iv3x80iWgTw3OP759bpQGTx4MKWlpbHXlZWVFBQUdHicz+dj5cqV3H777YAtjDRN6/T1x44di8vl6vRxqcq2bduYMGFCXw+jR2g+twkT4IwR5TFT1ZCC/mv66ern9tza98jOcCUsxNKEoMFvpMz34ET6Tg40ms8vGAx2eiHe60JlypQpPPPMM1RXV+PxeFi/fj0PP/xwh8elpaXx0ksvcd5553HOOefw6quvdklTkfRv+pOpqieQ0Wr9n4HuE+v15MfCwkLmzJnDrbfeyvXXX8+1117LuHHjuPPOO9mxY0ebx2maxq9//WsWLlzI1Vdfzeeff87999/fiyOXSPqeziZ3SlKLE6GCQ5/kqRQVFVFUVJSw7cUXX2yx3+OPP57weuLEiaxatapHxyaR9BZdWbH2x2g1yTF6Iiw+1ZAZ9RJJH/DlIT8bt3ctiutENwH2Z06ECg5SqEgGJKlut968qwFd1wf0ilXSkhPBJyYLSkoGHP3Bbl3bYGfwxzPQVqySlpwIPjEpVCQDjv5QeTjbq3eqRL9kYDBxTCF3FY8jJ9NDg98gJ9PDXQMscVWavyQDjv5gt54yxsvG7f5+V3Ms1c2K/YGB7hOTmopkwNHZRl19wcghnn63Yu0PZkVJ3yM1FcmAo79UHu5vK9YTIRxWcvxITUUy4DgR7NZ9QXl1kwwukHSI1FQkA5L+pgX0B06EcFjJ8SOFiiTlkc7h1KC/mBUlfYs0f0lSGukcTh2kWVGSDFJTkaQ00jmcWkizoqQjpKYiSWmkc1gi6V9ITUWS0vSVc1j6cSSSriE1FUlK0xe1kqQfRyLpOlJTkaQ0fdE/RPpxeh+pGQ4cpFCRpDy97RzuD7XDBhJRzbArvWUkqYcUKhJJM06UJL9U0Q6kZjiw6BOfypo1a5gxYwbTpk1j+fLlbe73wAMPUFJSEnt96NAhbrnlFq666ip++tOf0tjY2BvDlZxgnAg9L1LJbyQj/AYWvS5UysvLefLJJ3nttddYvXo1b7zxBnv37m2xz9133826desSti9atIjZs2ezdu1axo4dy3PPPdebQ5ecIJwISX6p1HOmP1SVliRPr5u/Nm/ezOTJk8nOzgZg+vTprF27lnvvvTe2z5o1a7j88stj+wCEw2G2bt3Ks88+C0BxcTE//OEPuf/++3t3ApITgu7w46SKeak1UslvJMu/DCx6XahUVFSQn58fe11QUMD27dsT9rnjjjsA2LZtW2xbTU0NXq8XXbeHnJ+fT3l551X1srKyrgw7pYm/TwON/jq3Lw/5+ePWWlQNHJrCoYogT/9+K1d/N5uRQzxA384tzWFS6wvi1I8ZK0KGRYZH75ZxdeYcCnDZOA+bdzVQVRsg26szdZwXpekbtm375rjH0t301+9kshzv/HpdqFiWhaIosddCiITXbdHafskc15yxY8ficrk6fVyqsm3bNiZMmNDXw+gR+vPcVn6yifR0d4KzPxAy2PGNwg+KJvT53ESa7VPRdCWmHTgQ3FY0jgnHqU11ZW4TJsAPio7rsr1CX39uPU3z+QWDwU4vxHvdpzJ48GAqKytjrysrKykoKOjwuNzcXHw+H6Zpduo4iaQvSHXn84ngN5L0Db2uqUyZMoVnnnmG6upqPB4P69ev5+GHH+7wOIfDwcSJE3n33XcpKipi9erVTJ06tRdGLJF0nv4QliyLQ0p6gl7XVAoLC5kzZw633nor119/Pddeey3jxo3jzjvvZMeOHe0eu2DBAlasWMGMGTMoLS3l5z//eS+NWiLpHF0NSy7dVc7cZZv4yaPvMXfZJlkaRtLv6JPkx6KiIoqKEg2oL774Yov9Hn/88YTXQ4cO5b/+6796dGwSSXfQlfIyMrNcMhCQGfUSSQ/RWfOSzCyXDASkUJFIUoTO5o6kch6M5MRFChWJpBdoLgDOPlnQPDI16tw3Laj1BQkbFpqqcNKg9FbPJ01lklRE9lORSHqY1ups/XFrbQsnfPElI2j0G1RUNxE2TBQEhmlR6wu02Lfkg72ETZOqugD7j/ioqgsQNs0+KbMikcQjNRXJgCYVTESt+Ur8AVr4SiaOKSTT66QpEMa0BLquku11oWlKi30PHKmn0R9GQUFVFAxDUNcQwjDre3VuEklzpFCRDBhamJiG57Fx68GkTURdEUDJHNOar8ShKa36SvxBk5MLvC2qTjTf1zAFAtBUez9FAcMSGKZod7wSSU8jhYpkQNCaj+GtDXvISHfiTbPL8rQXTdX8+EOVPv79d5+Q5nYwbHBGq8IiWb9Ga4mQYVMwpKBlImSySZMOTSUgwBICVQFLAMLeLpH0JUl/A03TZOfOnezevRsh5GpIklq0VsrdtASN/nDCfs2jqaLJho/97hNqfAFMU9AUMKhrCGFGkhbb6jWSbPn41hIhLZNWEyGTTZocNjiD7AwXuqZiWqBrKtkZLoYNzjjeWymRHBdJaSqlpaXMmTMHTdOwLAuHw8Fzzz3HqFGjenp8EklStGpi0lXChpWwLbrqL91Vzivv7ORAuQ+HpmAYFrqmUFnrR1UUFBQ01TYztaXhJBsC3Foi5NQz3a2a1pJNmoyWi8/Lcsly8ZKUIimh8sgjj/Doo4/Gam1t3LiRBQsW8Prrr/fo4CT9m+50knd0rtbMRukenfrGMIFQYp+Os4fn2dFYvqBtOrJAAKYQaIotiBy6iiVswQStC4vO1PdqngjZXnnxZJImu5KxL5H0Bkmbv+KLN1522WX4/f4eGZBkYNCd7WqTOVdrZiOHpvEPl5/RohLvjq+q0HUF0xJoqoKqKmiagrDAdn/bAkYIyM6w/TGtCYu+bjs8cUwhj/30Al6adyWP/fQCKVAkKUFSmsq4ceN49913mTFjBgAfffQRI0eO7NGBSfo33VlyJJlztbdy/0Gz8y0r2U6GR8ehqximhaqApiiggqaqWMJEVRQy052kubQ2hYXUFiRdwbIEIcP+jjmbtUcYCCQlVD766CNWrFjBokWL0HWdqqoqXC4X77//Poqi8Omnn/b0OCX9jO5sV9sZ30UyD/So2So7w0VljR8LAUKgawo5GS7uKh4H2MLs4BEfYdP2t0Qd8M1zS6QQkbRH2DAJGxZhwyIUtgibJgjI9DpPXKEiKwNLOkt39hPp7t4kUSe3risMynZTUx/EsARDBqVz+zVnJQiJ50u249V1XA5NlkKRdIhpWhEBYhI2BCHDwLLXLCcMSQmVoUOH8te//pW6urqE7RdffHGPDErS/4k+uAMYxx2d1NlzdeTUb262GvmdnFbNVrJqsKQ9hBCEDAsjKkDCJoZlnVACpDWSEipz5syhtLQ0oX2voihSqEjapDV/w9nD8yj5YC/LSrZ3KhqsM76LZBMSkzFbdacJT9L/CRsmQnVQ3xCMmbGEgBNchrQgKaFSVlbGhg0bcDqdPT0eyQAi/sF9vFV1k/VddEa76EqYcqq1BO5tUqGWWm9gWoJwOM4XEjFjVdf58TVLqJUkklRI8emnn45hGN120TVr1jBjxgymTZvG8uXLW7y/a9cuiouLmT59OvPmzYtde9WqVVx44YXMnDmTmTNn8uSTT3bbmCQ9S7LZ58dLeXUTrmbOz9a0i66GKZ+ICYbRqgM/WrCWf//dJxyq9B13mHgqIYRtumoMhKn1BamobqK8upGq+gD1TSH8IQPTsv0isppIxySlqdx8881cd911nHfeeej6sUP+/d//vdMXLC8v58knn6SkpASn08msWbOYNGkSI0Yc+0O9//77eeSRRzj33HOZO3cuK1asYPbs2ZSVlfHggw9y7bXXdvq6kr6lt0xJyWoXxxum3B/pasHMqIYZCBmYwq6G7NQ10j2Ofuljinemh8L2T0uasbqNpITKM888w4UXXsgpp5xy3BfcvHkzkydPJjs7G4Dp06ezdu1a7r33XgC+/fZbAoEA5557LgDFxcU8/fTTzJ49mx07drBv3z6ef/55Ro0axfz588nKyjruMUl6nt4yJRhY+ZcAACAASURBVCXr1O/uMOXjpafNSl01P8YLX8MU6KqCZUFtQ5B0jyPlfUxCiMRoLOlM73GSEiqqqrJw4cJuuWBFRQX5+fmx1wUFBWzfvr3N9/Pz8ykvL4/9/uMf/5jx48ezdOlSFi9ezBNPPNEt45L0LN0ZDdYeyWoXqeQv6Y0ujl2NZIsXvtFkUUUhVlMt1XxMpmkRMkwMwyIYtiOzpBbSuyQlVM466yw++OADLrnkkuO+oGVZLXpFxL9u7/1nn302tv2OO+7gyiuv7PT1y8rKujLslKa9OlKpggJcNs7D5l0NVNUGyPbqTB3nRWn6hm3bvmnzuK7MTQG+f76bLw8JNu/y8dTvt5Lt1ZkyxsvIIR4Azj5Z8MetAfwBu7dJ2BRYJkw90x275peH/Gze1UBtg9Hi+O4gep1XNlQSNgwUVJoirsuwYfHKms9QmvLbOUPyHDxcg9up0GiEYtuEEBw8HGj3Hqc5TGp9QZy6ischqAvbq3xdU6ipb2hxz5rPradQVRVFUbCEgoWSIERMy+pR30d3PUNys9JQReo5/Y/3s0tKqGzevJkVK1bgcDhwOByxB31XMukHDx5MaWlp7HVlZWVCqPLgwYOprKyMvT569CgFBQX4fD5WrlzJ7bffDth/EJrW+WzUsWPH4nK5On1cqrJt2zYmNG92nqJMmAA/KEp+/+OZW+mucjZu346u6+RluwiGTTZu93PGiDOYOKaQCRPgjBHlMY1mSEGiRtPR8cdL/NyeW/se2RmuhMVUmhA0+I1u+2yHfbKphWYWCBkMy/O0e40vq3bz1oY9mJaBQ1fxuB0EQyYet86QgsxWtcCe+k4aZsSMFbYIRUxavR3SW1ZWxtixY7vlXFleJ15PakXUNv/sgsFgp4VoUkLllVde6dzI2mHKlCk888wzVFdX4/F4WL9+PQ8//HDs/aFDh+JyuWKTe/vtt5k6dSppaWm89NJLnHfeeZxzzjm8+uqrXdJUJCcGyTri2xIQvZn42BumuK6YH0t3lbNx60Ey0p00+sOEDQvTEvzD5Wfwg2mju21srRF1phtmtLyJGSvyKUltks6oX7t2Lbt27eLuu+9mw4YNXY7AKiwsZM6cOdx6662Ew2FuvPFGxo0bx5133sl9993H2WefzZIlS3jooYdoaGjgrLPO4tZbb0XTNH7961+zcOFCAoEAp556Kr/61a+6NAbJ8dEfchWON9qsNxMfe8Pf1JVItqhg9aa5yIlUaw6EDHZ8VdWiSOfxEC2waJiWrYVIAdKvSUqovPDCC2zatIkjR45w++2385vf/Ib9+/fzs5/9rEsXLSoqoqgo0Q7y4osvxn4fPXo0b731VovjJk6cyKpVq7p0TUn30BWncrwQSnPpCAT+oNmjAul4V/+96cjvrdDlzkay9YRgjUVjmRZG1IwVKbAoZcjAICmh8s477/Dmm29y0003kZOTw4oVK7j55pu7LFQk/ZfOmoXihZCqwMFyHwD52e4eLdCYzOq/PY2rt6LVoqRitePuEKxRP8iJHo1lWhZ1vhDV9QGq6gPU1AcYfWoOF4wb2tdD63aSEiq6rieUaMnMzExIgpScOHR29RovhL6tbERVFRBQ1xhiaL63x/wUHa3+O9K4BlriY1forGCNmrEsxUFNfeCEMmMFQyZV9X5q6oNU1fmprg9SXR+gui5AdX2AWl8Qq9mN0D9SOP+xwTj0gVX+PinJcNJJJ/HBBx+gKAqhUIiXX36ZoUMHnoSVdExnV6/xQihsWGiqvUqN5jn0ZPLc8Trie0N7SGX/VEeCtbU+IUJAdV0TTcHuK+vU1wgh8DWFqKoLsPdQgMON+22BUR+whUedn8ZA8vPNSHOQm+Vh8lkDT6BAkkJl/vz5PPDAA3zxxRece+65nHPOOSxZsqSnxyZJQTq7eo0XQtHkOeJ6v3e3nyLZh3QqVCDujaTH42XimEI0VeHdzX+npiHIhtIDBEMGw0/O6pY+IWVfHWX9Xw5wtM7PoCwP0yadwtjhg7pn8ElimBY1Pls4RIVEdX2QqvpA7HfDtOKOqGvzXKqqkJvhIjfLTW5m5F/875nuWGOuLG9qhRN3F0kJlcLCQl555RX8fj+maeL1ent6XJIUpbNmoXghlOV1UlnjByAr3dXtBRo785DurMbVXFidPTyPHV9VdUnDKN1VzisbKvm26hCqArmZbhSnkjL9WuKjsXbsOcrr732BJQQKgj0HAuz+ezWzpo067od/2VdHeX39F2iaQrpLo64hwOvrv2DWNLpVsDQFwgmmqKhPo6rO1jTqG4JJ+3icukJ+TnpEWLjIzfSQm+mKCY+sdJdt4j2BSUqoHD16lNdff53a2tqE7Q899FCPDEqS2nTGLBQVQq+8s5Py6gYUBTRVwR8yOSU3vVvNPZ0JIuiMxtVcWB2q9LHz6yqyM1xke52d0jCi5wobBpYlQIHKWlvQ9kUtLdMShA0zkhdi/x42j9XGeutPe2kMhGOVn10OjSAm6/9y4Lgf/Ov/cgBNU47r3JYlqGsMxgRGdUxYHPsXCJpJnUsBMr0ucjNd5GW5jwmMLPtnXqaHr/bu7rbkx4FKUkLl/vvvx+12c+aZZyZk/UokyRIIGhTmelo8wLtDoES1iM+/rsKpq+RkuEj3OIC2TVrxGteBI/UYpsChqa32oW8urJoCBij2z5wMV6c0jOi5FFScDsWupSWOFWjsqbDlaCivYVoYhoVhRoRJJCW9rZX60To/6a5Eu79TV6mq8x/3mJI5dyhsJgiImLZRZ2sbNb4gppWcnuHQVXIz3eRkuiNCwv49L8tNbpaHbK8rZpaVdJ2khMqRI0f44x//2NNjkQxQOtIgjsdZHVv5myYIQSBkcqSqiWyvk7xsT7sP6eg1olpIW33om/tfwoaFFldUEZL3x0TP1WRAdoYrYg4UhI3u7dcS1UDChkUoZNmaURdCeQdleahrCCT0qAkZFnlZx18DLS/TTa0vgKYqGKbAMO3ER1VVeex3n1BTH8DXlHxtrHSPI8534SIvy5Pg08hIc8hFcS+QlFAZMmQITU1NpKWlTjVSSf+hPad4e36Q6J9/e0Kn5IO9hE2TuoYQiqqAKRBATUMIVVNwaBpnD89j7rJNCRrJsMEZFF8yolUtpKY+yKKXPibd42Dm1NNb+F8cukrIsHDGrWqT1TCi5wJId+uQ46G6LoAlBDmZni5pb8cq84pjeSFW94TyTpt0Cq+v/4IgJs7IvE1TMG1Sx20wzIgDvCbix4jXNKrrAxytDTRzgMeOpKFZd0VVUcjOsH0XeVlx2kaWh5yITyPePybpO5L6FAoKCrj++us5//zzcbvdse3SpyJJhvac4u1pMd8/392h8728uolGv4GCgqYqqIr9YBUCAiGL6VNPZePWg4RNk0Z/GAEEBByqbLB9KiETt0vj28pGgiF7NR+l0R/m9+u+4OLxQ6msbor5X9LcOiFfiDS3jhCihT+mPad+mkunoSmMZZrUNDQQNixUVUm6nlZUA4mWNAkbVo/2Bxk7fBCzptn+j6o6P3lxEVqBoNGqsPjmcDXLP/yI2oZgp8bldKiclJfOKYMzY4IjL+oA9zrRVGma6g8kXftL5qVIukp7TvFlJdvbCe11d2g6K8xNo6rWjxaJuFEVBV1T0VQFr8fBjq+q0HWFusZjgscSgqaAQV6Wi0CDga8phKoqtGaaF8CWHYd58Lbzj1U0zs9g+mRbUDSPgGvp1G/g86+OoioKAqjTVBQFQiELRVVw6CrpHp2NWw9yxrCcBC3FsoRdzqRZg6neKGliCYGv0c4AD4ZNxg7PiwmO1R9+xW//+/NO5aJkpjsTwmrjw2zzstykuXVpmhogJCVU1q1b1+r2aLdGiaQ92gtD7ii0t6N8kuJLRvDFvmpMIdAVYrkT6R4HBblpsePDhoWqRAWP7Q9xOTRChomC0upTWlHscwXDVqsRb9GiilHNZFnJdhr9YdxODW+aXYDR1xSK+DIETl3FEoJw2E4CTY/MS1MV0twa7209wOjv5MQSCntSgIQNi+pIaG1rEVM19QEMM7kr65pCTsYxYWEE6hk94pQEM9VATPKTtE7SyY9RwuEw77zzDsOGDeuxQUkGHm2FIbcb2tv0TYdCZ+KYQm68/Aze3LDH9pfoKukeBw5NjflMaur9duKlIVAigsehqwTDJpqqkpfpoq4xRLiNyNP2FtDNNZOqWj+BoIHToZHu1jFNgcupoWsqaS77pwAUFIQQtj8kZFEe8FPtC1HXGGr7YkkihKApaBwzSdW19GnUd+I6HpeeoGHkZR3TMHIz3WSkO2MCG6I9R4Yc9zwk/ZOkhMr555+f8HrKlCnMmjWLn/70pz0yKMmJQ3tazLZt3ySVT/KDaaM5Y1hOmwmZz5dsJ82tU9cQwoiEQKWl6xiGYEh+OqZpMTTfy77D9Qmr86g/oCCn7UinqHnO49LRVZUsrxMhBKoCOZlufE1hO4zXtCvyNgVN/EE7RyU+fNW0LKzWfNatYFoWdQ2hBD9GfJhtVX2AYCjJ3AwFsryuBCHR3ETlcUkHuCR5uvRtqampoaKiorvHIjlBaS+ZMtkM/rbOEX+8YR6L/hqS740JpqjQGpTlprzGHxMmCpDmcXB38Tmtjs20BMGQQWFuGqpit7TNyXBRWeunqi6ApsLR2iZMyz6XrikIYftKINoqOyK8Ii16wc7NqKprRVi0U5ywLRy62qawyM10k5PhQtOkA1zSfSQlVJr3Pjl06BA333xzjwxIImnO8RZ27PD4OKF1yuAMFBSCIZOT8tO5fupwxo0YRCBkYFoCs1nioEPXOFTZmKB1qAooikJjwGRwXjpVtX5MIbAsETOlOTQQ2MmPqmIHEITCFv/61J9bhNO2R0aaI+K3SDRJRQWH1yNzMyS9S6d9KoqikJuby/Dhw3tsUBJJb3LeqALOOWMQpikQ2A9/wxSYEeFRXtPUZmjspRNO5vX1dm0sZ8RPoygK0yefQpbXRXV9gK++qWXf4XoCITOmpdj+G9veZQqBCWCaBOMcO6qqkBPJzYgJjDaKE0okqUKXfCrHy5o1a1i2bBmGYXDbbbdxyy23JLy/a9cu5s2bR2NjIxMnTmTRokXous6hQ4e4//77qaqq4rTTTmPJkiWkp6d369gkAwMhBKZlCwjLiggLAcI6tt0wLUzTsh/tSUZZ+QNGrChh1PGdk+nmYLmPYNiMCZ/VH37V4bmiGo3bqXHa0CxGnJydoGnI4oSS/kive+DKy8t58sknKSkpwel0MmvWLCZNmsSIEcccr/fffz+PPPII5557LnPnzmXFihXMnj2bRYsWMXv2bK655hqeffZZnnvuOe6///7ensIJQUelU+LfRwg7esqw8Lh0rp96OrPiEvm27S7n7T9/RUWNn8LcNM46LZfP/15NRc2x9sL1DWFMS6Brdua0qijU+5r44/a/MON7p3H2iEEIIdjxVSXrPzlIdX2ANKdOIBSmriFI2BBYwtYuTEugqXDJhGFcNfnUBGERX2rd49QQQH1jCNO0j8nPSWf86HyyvS6q64PsPVjDlwdrk3Z8x5Pm1mP1tsCOonI7NQxLkO6w+P4VZ8UKJ/7ho69Z9/F+AiEDt1Pn7OG51DaEW5SEP55S8a3NPRAyu1xyvqOxpEJZe0nvowjRu33ZVq1axdatW3nssccAePbZZxFCxHJevv32W2677Tbef/99AEpLS3n66ad5+eWXmTRpEp988gm6rnP48GF++MMfsmHDhqSuGwwGI6GOY3G5XD0zuT5g27ZtTJgwoVvOZa/o4W97Klj+x104dBWnQ4tkcAtmTRvNWaflUfZ1FSve/wJdU/AHDRoiGe2qCii2NnD5d0/hyvO/w8591az60x50TUHXVeqbwjQ0hvCmO9FVhZp6O+taACgCYRJxQgs8TgW324VpCoovtRcdb77/JYoKhimorPG3mrAYz7UXnsa1F54OwF+/rOCN9V8gFDAMiwZ/1xtJaapCbpYbl0PjSFUjpnnMXyIEuJwaCEHIsIhUj0HYRYnJy3JhGGF03cmsaaPYd7iedzf9HRTsmmIR+ZXu0cnNcMVKo0w++yQ+3nEYTVMSSqYkU4Y+vsy8aQmq6wIA5GbajvpkzxN/vhXvfRkbS9i0x3LTlSPBXw6eQt58P/q+ZmuFFvzD5Wdw5ml5HV+gHQVNBVAUVEVBUezw7Mh/KNFtzfxIrT3mFEU5dpkkFcLdX+xmzKj2Kx+07cJKfMPpUHGlWGmZ5s+Trjw3e31GFRUV5Ofnx14XFBSwffv2Nt/Pz8+nvLycmpoavF5vrI1xdPtApCkQTijXHf2SRv9QlNj/wLB06nxBwH4wR/94on9DQohjK/VIEl7ie8eq1Eb3e2vjXgJhEytSoBHs3JAV7+/hvpvTeXPDl9Q3BnHqGgfLGyLmIxHL/zAti7c27uWCc4ZS8qe91DUEYwUJy6ubCJsW9Y22M9q0rFgYr0NXY0UadU2hKSA4yekkZJj8cct+e38hcKkaR2s7FigA727ex46vqqiu83eqOKGqKjg0lZBhggBFjT6ABIqicNqQLH5xywSWvvZprJy/EjFVibj7pke2i8i8BOBrCpOZpqJqCuv/coCD5T5boMTKkNj7NvkN8jLdsZLwG7YeJDPd0aVS8RtKD+JxabicGpU1AbxpDhTsKgJ5Xhchw+Sjvx3inDMGoSpq7IGtqkrsQa0qCopqf/X++mUlgwelxXw6ChAIGXz6RQVXnu3mvR0V5Od4cEaSHi0gFDL4ZGc5U887ud2xKpEvePzDWYm8obYiMHoTHYNM78BZlPYEvS5ULMtK+FLYYZVKh+833w+69uUqKyvrwqh7FxM9VnQwGT4u/axbr7/v22pcDgV/s8+hzuenrKyMg0dqcTkUQiHFzvuIQ0SywP1Bg7KyMg5X1tnnMu1zRYVG2LAfuvEfoYhL1BARIeUPBBBCcKgigIiE3Tb5IRROLqnDsgT7D9d3uF/UdSEE5GaohAwAQciI+D4iIlcApik4crQ+Nj/DFPbq2Tq2T/w84qWfotiFFjVVA2FwtMaHJQQep46u2Q9NCz22inbqEYGrKYTCmi2MAFVTUFWFLFSChsHhg1/HXTNyX5XIYBRBMBAk3Q1g2qHN2LHMQggsM4SK4Gi1jyMH98YtTOJzdhI/57K95bidSou/1YNHBFeedRKf761o9f1vK+rZsf2vHX4eqcy2bdv6egg9yvHOr9eFyuDBgyktLY29rqyspKCgIOH9ysrK2OujR49SUFBAbm4uPp8P0zTRNK3FccnSH8xfvsYQ9U3JZTxHVdPu5KTtn7Yodx4Mm5yU7Wbs2LEJ7yt1DQkPUUVVwbLwOPUW+wI4mmxNxaHZr03LIvYYVpTY79HncH2TRdgUsaipzhYt0VSFay88ndxMF+99coBAMIzHpVNRE0jQkjRNRQiBpqroDid5OXbh1L8fqkVYxLQQhMDpVBk2OIuzx47l1N1/42B5PYqwzxF1rFuRRZBTU9E12zwkIsN3OhS8HgVVdZCR7mb/kTr8QQM1IrrCIQPTsoWZy2kHogTCBqpqYQoV3aHb/iMDmoJhcjI9TBw/rt37UPLxplhlgqZgMNbWWdcVhOIhGDbIyfZw7rnnJnVfh32yqUWlg0DIYFienSg67KScNt/vLnNtX9Cd5uZUpC3zV2fo9aynKVOmsGXLFqqrq/H7/axfv56pU6fG3h86dCgulysmLd9++22mTp2Kw+Fg4sSJvPvuuwCsXr064ThJ9zFt0imYpohEM9k/48udx7/vTXPEjlOViJAQcPl3h8X2NQyLpqBBIGSgaQrCiprhREIGe/Ns9mjdLauZNqQqxxIFO+LqKady9ZRTmTT2JK6/eDigEDIsMtIddt4I9oLezmgXeD06qqJw9fe+w4wp32FQloeMdCc5XieDstwU5qZxSqGXmy4fyaBsDzdeNoIMj8MWBoZJUzBMQ5MddBAMGlTW+jlc1cCR6kYOH22kvLqRYKTnS2VNgCvPP4WLzzuZOl+ImoYgvsYgjQEDf9C+V4GQQV1jkMYmg+mTv0OT38TXFIrc03DS/VeKLxmBEenZkuV12lFxQpCV7uxSH5f489nmvsRzdPS+ZOCiLVy4cGFvXtDr9ZKbm8tDDz3Ea6+9xvXXX8+MGTO48847Oe200ygsLGT8+PEsXryY3/72t+Tk5PCLX/wCTdMYP348Tz31FC+99BI+n48FCxYklOJvD9M0qaiooKCgIOaXSVVC4cR8hfaIzqk7KchNoyA3jW8rG6lvDJGT6WHmxcNjdvv490NhE7dTxbBsR6yuqQw/OQtFUfhT6UG27iqnxhek0W/QGDAIG7aAMCOhva2hRar3KoogO8PN5LGDuW7q6cy44DRGfyeHusYwlhDkZrrQdYVwXChvFIeuMGPKMSd9dNyFeWlU1PgxLcFJeV4KB3lI9zjI9roYlO3h1KFZ3HTFSCadNZjThmTxnZMy+erbWqrrg/iDBpnpLn509ZlMHFOIpqmcNMhLYV46u/dXU10fxDQFJw1K564bxvG9s4fw1be1+JrCqIpCfrabvGwPhilIdyn85PpzmTimkLOHD0Ig+OqbOkKGRZrbwQXjTsLp1KnxBRmUncaPrh7D9MmnMiTfy77DvoTtySSGDsn3xo5t9IfJy/KQ6XVimKJT52ntfM3HcvjwYSaOO6PLY01lDh8+zJAhA7euWfP5deW52evRX31Ff4r+6mvzVzzR4oQ1rRQljP6rbwglbZRKLE7oivUBj3bpy0x3xkxInZ1bfAhrYW4a084/hbGn56FrGg5dRdcVdu+rYd0n+ymvaiQvy8O1F5zG+NG9/6BL1oxyPF0xu3Ku7rjeQDYRDeS5QT+N/pKkFpYlqGsItlmYsLouEItk6ggFyPS6EmtNZUUaLUX+edw985Xbva+Kdzf9nTS3xvCTMvCHTP64+e/kZrlBKJR8uJf9h+ppCoTJSLc1k8NHG1i2cjt3xbUOTiU6alDW3efqzutJTlykUBnghMJmQr+M+MKE1fUBanzBFj6LtnDoql2EMK4jX7zwyMlwoXeyOGFrCXJgdxo8eKQWY+1GTEugKgqD89L4/qVnMPb0PBy6xp6DNfz5r99SWRegzhdEU8FhqgQaQjT6w1TXB1j88l/scFRBLFKt1hfCqWukexyxhl8Ar7yzk28rGwAYkp/O7declfDAbaubY7Ir+vhzpDlMRFp5u8e01qCsJhhkyfJtpHscndIkOmp2luw+EklHSKHSjxFC4A9a7D9c31Jw1AeoqG5KWssAYvkWDl2NJZJddN5QzhtZQE6mm4y0Y8UJWxMG5VWNncqgjk/IS3dpVFQ38HzJ30BRcTk0HA4HbtXu5OjQVSxLsOZ/viYvy40Q8Ls/7CQQNmj0GwRCJgqQ7XXidumUVzcdS6pshmkJjlQ3MTgvnTSXxsEjPp5+4zPqG0ORZDr4pryBp17/lH+eNR6Ap17/FH/QiCQO+inbe5TcLDfZXmeHK/rSXeX87p3POVjegENTycl04fMbHWoBzRuUNQYMaiM5SYNzPZ3SJDpqdpbsPhJJR0ihksKYpkWNLxgzR0VrTsVrG3beR2WH5wI7ampwXjrDCjNsbSNO01i+dje+pmCLMOKvv63n+osTI3aaC4O6hgCvvLMTRbF9JtFtr6//glnTaFOwbCg9SLpHJ93jwDQsTEvgcTlj/UcM0yIQsjDMsF2jK5J/8fKaz8lMdxEIG9Q1hDiWlgg1DSGUJHw8QkBljZ8srzPSstfuFR9rNmUJ/EF7lV7fGIw521XFjh4Du7xLToar3RV91KRU4wugKXao8dHaAJkeBV1X2tUCmjcoq/UFQQGnrkZqhiWvSXTU7CzZfSSSjpBCJcV4470v+OTzI1TW+qltCLZZHbc5LqdGXtQ0leVm59dVhAwTdySpTlPth6E3zck/Fp3V4vhqX4B0V2LFW6euUlXXMglz/V8OoGlKQmZ3dX0AUMiOZBu3lu2tKKApCg7ddpwLICfDhWXBoZpG/MGOo96EsLUIt8tPU8CI3J9mCZjJ3TK75lhjiDS3TpPfbHGcakFFdRPV9QHUSHZ5PIZxLAGz+Yo+aurava8aVbHDpbVIdrqFoCFgkZPVvhbQvEFZKGyiKMTucWvXTfZcrTU7S2YfiaQjpFBJIRr8YZav292qIMlMd9o9vzOOOb9zMt3UVH7D+ePHkubWE7KX5y7bRFa6M2FbW0ICYFCWp0XCY8iwyMs61vUwavL68kBNrIqRptnlNOxQYYE/ZJLu0nE4VLxpDgxTkJnmxKHbJqz4hlAOTeVonb0yrm/sWLuIlqdRgMYk6nYdS6VsHYem4nJqZGe4qGtoGW1nCXBHBG1Cgme0sVYc8Sv6eIe3ZdkFv6J+q2jWvBHJ82lPC2jeoMzt0nE77XbJrV23PZJpdpZsQzSJpD2kUEkhvB4HD/xoInsO1OJNc8Q5wN0JTaDiKQuVJzxkoiQjJOKZNukUXl//BUHMhGKFUcd51OQVrbgbfaaalt1JMd2j49RVHJqKN82JQ1do9IdJT3ORke5s9ZrRlXFNMJBUHS+BremoqkJ7B7T20I9/TwEK89LRVMjJ9OBrp1+7gsLQfC8Hy+1yKlGNxTRFLAs/uqI/e3gec5dtimgndsFJp8MupqgoIhJwYNdbUxSS0gLiG4zFm9Ia/QZhw0JTlQ5rabV2ruPZRyJpDylUUowLzxnKOSPyk85TaYuOhERzxg4fxKxptmmrqs5PXjNH+/q/HEDXFYJhQWa6I5b7oSpKpP+6SdiwaAr6qa4PMCjbjWEIfjB9TJtjjK6MlyzfhhoRBKqmoEQ6IkJc+SrA4VDJy/J0aO5xOlQMo2VypUKk8KNim7XUyAP5/a0HUVUlIQrODlSwa5jdXTyOp9/4jKaAgWFa6JqK26kyKNtNg9+gIBINtnHrQbvsiRCYwq6inJnuoL7Rik0kHJlXdrqWEMrcWn4I0GLbZd8dxpsb9sR6V+kSzwAAHPBJREFU3Kd7HGzcepAzhuW0e662BEVnWhwcb55Md5Kq45JIoTJg6UhItHVM8/cVBXRVRVHg5IIMDh7xYVoWwbBJfWMQI1LyXVUUCnI81PgsQoZFTqYnqT/0iWMKSfc4GJzroSloUusLEjYs3E4NTRX86w/Pj5mSonb+qGAwLdFCI1EVsCzIznAmrOZvvPwMAN7asMfWrnRbu9q49SBpLh2HpmBFtSCIaCUKBblpTBxTyH03n9euWWjusk2ETZO6RiPSQRI0jUhrYEG0VqZDU8j0urCMYxWTW8sPefqNzxBC4E1zJOSMuF06hbmeFjW1os76zuSadLRvquatpOq4JDZSqAxgWhMS7WELkGOOdF1TcTg0NFXB49I5UtVIfVM41lfdMO3aWXqkaGK6x4GmKeRkenjspxckfd1o1FG6+1iUUyhsQsRyd1czO//U807mj5v3URMJr42iKlCQ48EfMhmSn9FCAMxdtomCVh7IAoHHpdul8SM9ZYQAb7ojpjF0ZBY6cKSeRn841lfGtOxqxmZcPTNVsbUup64SEsQEQWv5IZU1AUAwKNsT2xbA4NvKBk4p9CZcO95Z35lck472TdW8lVQdl8RGCpUTlKh24XLECZBmjvR4Zk4dzvMl20lz69T6gliK3SnRsmwndFZW1woTQrxvJRgLm1UU0HV4vsTOeG8upM4YlsPj/7mVYCQ/RdcVBmV50DSFU3LTWxVqbeVhNPgN/nnWeH73zuccqmxEAU4u8HLbNWcmndBY3xhCCLvmmK5qKFixZEvb7AaapmFZgtqGIFlpSkwQtDausGFiCdh3uD7WiMqKtEiubbBDmaPEO+s7k2vS0b6pmreSquOS2EihkkJEH1CWJQgbVre1X436BxyahsOh4nSoMZ9IssRHBpmmIGxasda/CnYHyGRNXm2de8lyuzK1U1ftsFkRRmsllyN6nzxOHWGJWNmVjkJgoxqRadoP9qhpbPCg9C45qH+/fnfMnBY1w4UNKyHqTFHslXTMR6TY+4RNlSEFaQnjiq68GwNGLA7BruQMIFBVO1AhmgCZ7XW2mHNnck062jdV81ZSdVwSGylUUoR4O/FJuWkcqG3qMHmwLWwBouLUNXT9mDnreDvm9WRkULxvJTrOxqZwwgq0dFc5r7yzkwPlPhyaQk6GC01zUt8YwjAFpwzOTBBqrZVWeXfT3yOJjPZ1DVNQ3xCidFf7JVOaU7qrnBXvf5lQrj+K4FgEmhDgdmr4miws7A2aqmCZJJSJj88Pqa4LxMYXH2tgWaA7FJwOjUDIjAUJxM+5M7kmHe2bqnkrqTouiY0UKilCvJ1YiZilkmkVq2saDi0iOBxRM5btB+lvtLcCPRZOG4w544/WBcjP9lCY64n5cUp3lTN32SYOHKmnKWCQme6MlVLZuPUgToeGrpkxZ312hgtNpdP2+Ffe2RkTKK2FMOuqiiXs5mJNgTCDst3U1AcxLMGQQelcMMoZu17z/BBLCApyPCiqyuGjjQnnFQL8AYOMNCcvzbuyxbg6k2vS0b6pmreSquOS2EihkiK0ZidunqzYPCPdoavkZjgoyE3v7eH2CPF5K41+g1DYRNdMpp53ckzompaICEwFy4LahiBDBqVTUd2UoO0FQyaWENT6gviaQliWHU4cNsxIX3U7WRMhcDn0Ttvjo4Uno6I7XrBEfTzZXjcCqKqzWyGP/E5O7OHXvGVrvBY4d9mxrorRlJxo4qeqKFiKiIUmt0ZnNMqO9k3VvJVUHZdECpWUofkq3eXUEEIwtCCDrHRnm450y0q+YGQqE+/w9geNSHFLyEh3snHrQfwhg0FZdhKoHX12zD8R1WbitT3DtH0bRqTDoVNXCZu28ztkmOiqnctSWWvX/xqSn9HpMWuRKC+lmZYyeFB6LJItEDIYfWpup6Lh4s07Md9K5H+hSBmbpkCYucs2yRW6JOXo9XbCktaJb78aNc1YFlz1vVPxptmVd9uKzOrvlO4q5+k3PuPL/TU0BY2IT0Ihw6PZ5fR1JVbWJDvDhRB2HomI+Cei9vTy6iZcDs12dFvH2hPbGoQSyxWx2xmDqto/fY3hTtvjh+SnA3ZNtYjOE6O6LkCDP9zlaLiJYwq5q3hcwucd1YgiU8Hl0GL5GaW7yjt1fomkJ+l1TeXQoUPcf//9VFVVcdppp7FkyRLS0xPNN6FQiHnz5lFWVobb7WbJkiUMHz6ccDjMpEmTGDZsWGzfkpISNE1rfpl+R7yd+JvKRgpy0/jRjFFM6IOuhL3NK+/spL4xlFCw0bQE9X6T3Gz7AerQbM1C15UW/olo35PC3DQOVTbYdbyUxLr30egr+zltl9MPGxYOTf3/7d17UJTnvQfw795YcBdF7IJglMmMtxjiVMWQE6ekTjzAgmiKsYKX4UxqtI5taK3WS05jbcYoOR0n9XiZOMqYMspJMseB2ALm0prOmJ6IxFO8EGLQ9ESJ7OJiIte9PeePZV9Zlsvusrzswvcz44z78r67z+9d2N++7/M8vweREWq/v+3/W/bjUjl8AUjr3U/QR6C1w4a799qhVikwJU4/2FNJeg8sAIBJEyJdlZgVrhhEdwabOD6S8zMoJMmeVPbs2YPVq1cjOzsbhw8fxpEjR7Bt2zaPfUpKShAVFYXKykpUV1dj586deOedd1BfX4958+bhxIkTcjdbFmP1PvEdc6s0b6Zn34S9+85el82BqZOjkfvD6VLnbM/+CbfcH07HaycvQkBArVTC6nzY7+Au2eLsHo01xeD6ItNpdQ2F9lfKY/EozJuPM+e/xPVb96CAqyZYW3flZJXKlbgcDqdPs737miXe2NyOuJhIGCZGSZUG0P067ttrnJ9BoUbWpGKz2VBdXY3Dhw8DAHJzc7F27VqvpHL+/HkUFhYCABYuXAiLxYLGxkZcuXIFFosFubm5UKvV2Lp1K5588kk5Q6BhIlU9Vihg7zGUquctJF86lcdFqtFldcDucEp9Hm7K7smaKpXCoxCk+/aUv/Wk3D+7dvMeVD2WFxDCVWXA7hA+X030NUtco1Ki5UEXpsZHQxepxh1zK6x2JyLUnmvecH4GhRJZk0pLSwv0ej3UatfLGgwGNDV53w82mUwwGAzSY4PBgLt370KhUODZZ5/Fxo0bcePGDbz44os4e/YsYmNjZYuBgi/RoMPtplbAKVwLZQkBp3CVNfF3QuW0yeOlAQ/uD2Gn03UrTKtRQ6NWwOGE1xyP/upvjddHoKPL0W+SOXP+S1fdMCcAKKSrLIfT+XDQhQ9XE32N/ps4XgtTSwc6ra75GOMi1bA+cK3/0ldSJAoFw5ZUKisrsW/fPo9tSUlJXhPw+pqQJ4Tw2C6EgFKpRF5enrRtzpw5mDt3Lj777DMsWbLE53ZdvXrV533DRe/hqeFm0awIlFuALrsTDqfriiJKrcDyf4nFzMRIoP02ampu+/RcTzwiUFndiY5O17d4dCenCToVtBGuK5ROq8CmzO4vIt3P/dZHZtjsdiigRLsd6LI68W2bq2imEK6qxle+bEaMToWshTGYmei6Zfb1Ny2IigAedDjhdDysqiwEEKURaGtvh9XuRHSU2uN9+qKxAyc/qsL9Vjti9GoonK5imhE9qhzY7E4YxqugVthx734nYvRqPDFNh7qvO/HVN51QAIiNVuPGlzegaPft/Mgl3H8nBzKaYwOGHt+wJRWj0Qij0eixzd3R7nA4oFKpYDabERcX53VsfHw8TCYTpk1zlWlvbm5GXFwcysrKMH/+fGm7EAIajfdaIgNJTk6GVqsdfMcwUVNTgwULFox0M4ZkwQJgxvQmr8lsivbbPsf28NZVB8aPHwcFFPiuvVVa16TnEN+pk6K8nvdI1QeukjPdX2but7UBCs/bZ67tDlTWtGLG9BlIeSweUy+65pRERbpKv3RZHd23vxSYOEGHLpsDGggU5MzFgh4z/Surq6HTRWJSjKu8TJfd9UVKpVZLs8Q1EB7l8d3Hfn6nFgnf00r7/aW2Q2pPKBgNv5P9Gc2xAd7xdXV1+f1FXNYxqhqNBikpKaioqAAAlJWVIS0tzWu/Z555BuXl5QCAS5cuQavVIjExEfX19SguLgYA3Lx5E3V1daP6DR5LUh6Lx2ubFuH4y/+K1zYt8rtkyptnatHyXQeio9RwOJzo7LJj5bMzXKVclOi+Qul/iG987DiPpYxtdid69PNLi3sBQHunq48EeDgUXKVSIPF7OiQadIjRR+CRuGi0drgGAfRODGfOfwmlClL1hMgINfTjNBivj8DE8VH9Huc+tmflhcgItbTWPVEokH301+7du7Fjxw4cPXoUCQkJOHDgAACgtLQUJpMJhYWFWLduHV555RVkZ2cjIiICr7/+OgBg8+bN2LVrF5YuXQqFQoGioiLo9b4P2aTRqb9S6Fca7nmVze+vf6Z3PSmVUiGNPuvJPbTX3UfSV8mQnyx7YsCk2GRph0bledvXXS350NaBJ0myQi+FOtmTypQpU1BSUuK1PT8/X/q/VqtFUVGR1z56vR4HDx4c1vZR+Bnog7b3iDF3bbDeI7x6J4eE7+nwf03fPbxa6Z72olIqoFYqPUZc+TsUPD52HBpNnmvB+DqKixV6KdSxTAuFnd5Df6O0rr6FwT5oB1sxsHdyKH3/c7z9QT0cPWbgQwDjItWDjrgaaHhy7g+n42BptTSqy59RXKzQS6FudNb9oFGrd/9Jy3cd+K7VitZ2V1mUgfpO/O2PyE+fjX9/4SkkJUS76q4plZgaH42XVs3zaSJjzzb2LKeS8lg8jAtjBu0/6Yu7hEsgxxLJgVcqFNJ6f+N/0Gb16j8BXEsAROsiBuw7CaQ/IpAqB74sdzszMQr5OYENMhmrlRcoPDCpUMj6orEDf6nto3TJRM+yKu5O7v/cunjA55OrP6Jn8mrrsEmrTDZZOvxeDIwo3PD2F4WsT+pavW5XaVQKtHwXWCd3z0rQgw0xHgr38OS2Dhu+udeOji4H7A7XEtH/UVLNqsI0qvFKhWTna42t+612TIrxnKg6MVoL8/3OgDq55Vox0N2Z3tdttfYuB4789/9is/Hhap7+1hwjCmVMKiSrwUZg9RSjV3uN6lKrlXgkXo/xOm1AiUGO/gh38tpz/H+kbe6VG4UAmu93Stv9OR9E4YBJhWTlSye229OP6fGX2g6v4bM/WfZ4yH/g9mxf7/J2Pdez9+d8EIUD9qmQrNyrM/bU3wismYlRYT18NjKiO053EhG9tsO/80EUDnilQrLydwRWOA+fzV08Hf/1fr1rnfnuhKJUuLYDbQA4Q55GH16pkKzkGoEVCvLTZyMvfRZ0URrXao1RGuSlz0J++mxpn7F0Pmhs4JUKyUquEVihIj99tkcS6W2snQ8a/ZhUSHbhfEtrOPB80GjC219ERBQ0vFKhMYETDInkwSsVGvUGqxpMRMHDpEKjHpfgJZIPkwqNepxgSCQf2ZNKY2Mj1qxZg8zMTGzatAltbW397nvhwgUUFBRIj4UQKCoqQmZmJrKyslBTUyNHkynMuasG98QJhkTDQ/aksmfPHqxevRpVVVVITk7GkSNHvPZxOp0oLi7Gli1b4JQWCQfOnTuHhoYGVFRU4PDhw9i5cyfsdruczacw1HOCYWu7FV83PUBjczsetFmH1K/iXu/+J3s/wK6jF9hHQwSZk4rNZkN1dTUyMjIAALm5uaiqqvLar6GhAQ0NDXj11Vc9tn/88cfIysqCUqnEo48+ioSEBFy+fFmWtlP4ci/Bq1IpYb7fCUCBuIlRsDscAXfYs/OfqG+yDiluaWmBXq+HWu16WYPBgKYm7z/CGTNmYO/evfj00089tptMJsTFxUmPDQYD7t6961cbrl69GkDLQ9tovg0YrNgUABROK2L0SkSolYCwwWEHbHYn3jp7GYp2g1/P99ZHZtjsdiigRHv3xbK/z8X3LTyN5tiAocc3bEmlsrIS+/bt89iWlJQERa864L0fD8TpdHrsL4SAUunfxVZycjK0Wu3gO4aJmpoaLFgQ2FrnoS7YsR2p+gAx0VqP36FxQqC1w+736wz1ufi+hafRHBvgHV9XV5ffX8SHLakYjUYYjUaPbTabDampqXA4HFCpVDCbzR5XHoOZPHkyTCaT9Li5udmv42lsC2ZFYFYXJuqbrH0qGo0GKSkpqKioAACUlZUhLS3N5+PT0tJw9uxZOBwO/POf/8RXX32FJ554YriaS6NMMCsCs7owUd9kL9Oye/du7NixA0ePHkVCQgIOHDgAACgtLYXJZEJhYWG/x2ZmZqK2thbLli0DAOzduxeRkZGytJvCXzArArO6MFHfZE8qU6ZMQUlJidf2/Px8r22pqalITU2VHisUCmzfvh3bt28f1jbS6BXMisCsLkzkjTPqiYgoaJhUiIgoaJhUiIgoaJhUiIgoaJhUiIgoaJhUiIgoaLicMJEfuCwx0cB4pULkI1YmJhockwqRj7gsMdHgmFSIfMRliYkGx6RC5CMuS0w0OCYVIh+xMjHR4JhUiHzkXpZ44vgotHbYMXF8FDbmzuXoL6IeOKSYyA+sTEw0MF6pEBFR0DCpEBFR0DCpEBFR0Mjep9LY2Iht27bh3r17ePTRR/H73/8eOp2uz30vXLiAY8eO4a233gIA2Gw2pKamYurUqdI+Z86cgUql6vN4IiKSl+xXKnv27MHq1atRVVWF5ORkHDlyxGsfp9OJ4uJibNmyBU6nU9peX1+PefPmoby8XPrHhEJEFDpkTSo2mw3V1dXIyMgAAOTm5qKqqsprv4aGBjQ0NODVV1/12H7lyhVYLBbk5ubixz/+MS5evChLu4mIyDeyJpWWlhbo9Xqo1a67bgaDAU1N3sX4ZsyYgb1792LChAke2xUKBZ599lm8/fbb+O1vf4tf/vKXsFgssrSdiIgGN2x9KpWVldi3b5/HtqSkJCgUCo9tvR8PJC8vT/r/nDlzMHfuXHz22WdYsmSJz89x9epVn/cNFzU1NSPdhGHD2MITYwtfQ41v2JKK0WiE0Wj02ObuaHc4HFCpVDCbzYiLi/P5OcvKyjB//nxMmzYNACCEgEaj8atdycnJ0Gq1fh0TympqarBgwYKRbsawYGzhibGFr97xdXV1+f1FXNbbXxqNBikpKaioqADgShJpaWk+H19fX4/i4mIAwM2bN1FXVzeq32AionAj++iv3bt345133kFWVhYuXbqEX/ziFwCA0tJS/OEPfxjw2M2bN8NisWDp0qUoLCxEUVER9Hq9HM0mIiIfyD5PZcqUKSgpKfHanp+f77UtNTUVqamp0mO9Xo+DBw8Oa/uIiChwY6agpBACAGC1Wke4JcHX1dU10k0YNowtPDG28NUzPvfnpfvz0xcK4c/eYezBgwf44osvRroZRERhZ+bMmYiOjvZp3zGTVJxOJ9ra2qDRaPwaxkxENFYJIWCz2aDT6aBU+tYFP2aSChERDT9WKSYioqBhUiEioqBhUiEioqBhUiEioqBhUiEioqBhUiEioqBhUiEioqBhUglxjY2NWLNmDTIzM7Fp0ya0tbV57WO1WrFt2zYYjUb86Ec/QkNDAwCgra0NhYWFyMnJQU5ODv785z/L3fx+nT17FllZWUhPT8epU6e8fl5XV4fc3FxkZGTg5Zdfht1uB+Db+RhpgcZWU1OD559/HsuXL0dBQQHu3Lkjd9MHFWhsbtevX0dycrJczfVLoLGZTCZs2LABzz33HPLy8nD79m25mz6oQGO7ffs21qxZg+XLl2PdunW+/U4KCmkbNmwQf/rTn4QQQhw6dEi8/vrrXvscP35c/OY3vxFCCHHx4kWxcuVKIYQQBw4cEPv37xdCCNHc3CwWLVokzGazTC3v3927d8XixYtFS0uLaGtrEzk5OeLGjRse+2RnZ4vLly8LIYTYuXOnOHXqlBDCt/MxkoYS2+LFi0VdXZ0QQoh3331X/PSnP5W38YMYSmxCCNHe3i7y8vLEzJkzZW23L4YSW0FBgTh9+rQQQojTp0+LwsJCeRs/iKHEtnXrVun/f/zjH8WvfvWrQV+PVyohzGazobq6GhkZGQCA3NxcVFVVee13/vx5LFu2DACwcOFCWCwWNDY24sknn8S6desAAJMmTUJMTAyam5vlC6Afn3zyCZ566inExMRg3LhxyMjI8Ijrzp076OzsxPe//30AD+P29XyMpEBjs1qtKCwsxOzZswEAs2bNwjfffDMiMfQn0Njc9u/fj4KCAtnb7YtAY7NYLPj888+lVWlXrFghLecRKobyvjmdTrS2tgIAOjo6EBkZOejrMamEsJaWFuj1eqjVrmLSBoMBTU1NXvuZTCYYDAbpscFgwN27d7Fo0SIkJiYCACoqKmC1WjF9+nR5Gj+A3u2Ni4vziKuveJqamnw+HyMp0NgiIiKwfPlyAK4/5EOHDvm1TLYcAo0NAD766CN0dnYiMzNTvgb7IdDYvv76ayQmJmL//v1YsWIFXnrpJb9Xox1uQ3nfCgsLcfLkSfzgBz9AcXExXnzxxUFfb8yUvg91lZWV2Ldvn8e2pKQkr+KXfRXDFEJ4bBdCeBR/q6ysxGuvvYbjx49LH8gjyel0erW35+P+ft57P6Dv8zGSAo3NzWq1YseOHbDb7di4caM8jfZRoLGZzWYcPXoUJ0+elLO5fgk0NrvdjuvXr+PnP/85du7ciXfffRc7duzoc82okTKU38nt27fjd7/7HZYsWYJz587hZz/7Gd57770B/+54pRIijEYj/va3v3n8Ky4uxoMHD+BwOAAAZrMZcXFxXsfGx8fDZDJJj5ubm6X9SkpKUFRUhBMnTki3Vkba5MmTYTabpce94+r9c3c8sbGxPp2PkRRobIBrYMX69etht9tx9OjRkPvGG2hs58+fx/3796UOXwBYvny5dFslFAQam8FggE6nw+LFiwEAS5cuRW1trXwN90GgsVksFty8eVO6Ys7IyIDZbEZLS8uAr8ekEsI0Gg1SUlJQUVEBACgrK0NaWprXfs888wzKy8sBAJcuXYJWq0ViYiI+/PBDnDx5EqWlpZg1a5asbR/I008/jb///e+wWCzo6OjA+++/7xHXlClToNVqUVNTAwAoLy9HWlqaz+djJAUaGwBs27YNSUlJeOONNxARETEi7R9IoLGtXLkSH374IcrLy6Xf0/Ly8pBaCjzQ2KZNm4bJkyfj448/BgD89a9/xeOPPz4iMfQn0NgmTpwIrVaLS5cuAXCNTtTpdIiNjR34BYc+toCG0+3bt8XatWuF0WgUL7zwgrh//74QwjXK5I033hBCCNHZ2Sl+/etfi6ysLPHcc8+Jq1evCiGEyMnJEYsWLRLLli2T/tXW1o5YLD299957Ijs7W6Snp4tjx44JIYRYv3691L66ujqxYsUKkZGRIbZs2SK6urqEEP2fj1ASSGzXrl0TM2fOFFlZWdJ7tX79+pEMo0+Bvm89heLoLyECj62hoUGsXbtWZGdni1WrVolbt26NVAj9CjS2f/zjH+L5558XS5cuFatWrRLXrl0b9LW4ngoREQUNb38REVHQMKkQEVHQMKkQEVHQMKkQEVHQMKkQEVHQMKkQhRghBLZv344TJ06MdFOI/MakQhRCGhoaUFBQgHPnzo10U4gCMvKFoIjGgE8//RQHDhxAQkICbt26haioKGzYsAElJSW4desW0tPTsWvXLpw6dQorV66UCoEShRteqRDJ5MqVK9iwYYNUouTYsWN48803cebMGZw+fRpNTU145ZVXkJOTM9JNJQoYr1SIZPLII49gzpw5AIBp06YhOjoaERERiI2NhU6nw7fffov4+PgRbiXR0PBKhUgmvYtEhsIyBETBxqRCRERBw6RCRERBwyrFREQUNLxSISKioGFSISKioGFSISKioGFSISKioGFSISKioGFSISKioGFSISKioGFSISKioPl/9steMpz0ID8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and then using the method to regplot of seaborn , can create\n",
    "# 然后可以使用 seaborn 的 regplot 方法，\n",
    "#    它可以做一个散布图，并加上一条线性回归的线\n",
    "sns.regplot('m1', 'unemp', data=trans_data)\n",
    "plt.title('Changes in log %s versus log %s' % ('m1', 'unemp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xf023908>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在探索式数据分析工作中， 同时观察一组变量的散布图是很有意义的， \n",
    "# 这也被称为散布图矩阵（scatter plot matrix）\n",
    "# 纯手工创建这样的图表很费工夫， 所以 seaborn 提供了一个便捷的 pairplot 函数， \n",
    "#  它支持在对角线上放置每个变量的直方图或密度估计\n",
    "sns.pairplot(trans_data, diag_kind='kde', plot_kws={'alpha': 0.2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意到了 plot_kws 参数。\n",
    "# 它可以传递配置选项到非对角线元素上的图形使用\n",
    "# 对于更详细的配置选项， 可以查阅 seaborn.pairplot 文档字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x144feec8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792.225x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分面网格（facet grid） 和 类型数据\n",
    "\n",
    "# 要是数据集有额外的分组维度呢？ \n",
    "# 有多个分类变量的数据可视化的一种方法是使用小面网格。\n",
    "# seaborn 有一个有用的内置函数 factorplot，可以简化制作多种分面图\n",
    "\n",
    "sns.catplot(x='day', y='tip_pct', hue='time', col='smoker',\n",
    "               kind='bar', data=tips[tips.tip_pct < 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x141c5748>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 除了在分面中用不同的颜色按时间分组， 我们还可以通过给每个时间值添加一行来扩展分面网格\n",
    "sns.catplot(x='day', y='tip_pct', row='time', col='smoker', \n",
    "               kind='bar', data=tips[tips.tip_pct < 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x136e7108>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='day', y='tip_pct', row='time', col='smoker', \n",
    "               kind='bar', data=tips[tips.tip_pct < 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13c76248>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='day', y='tip_pct', row='time', col='smoker', \n",
    "               kind='bar', data=tips[tips.tip_pct < 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14f586c8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# factorplot 支持其它的绘图类型，可能会用到\n",
    "# 例如， 盒图（它可以显示中位数， 四分位数， 和 异常值）就是一个有用的可视化类型\n",
    "sns.catplot(x='tip_pct', y='day', kind='box', data=tips[tips.tip_pct < 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用更通用的 seaborn.FacetGrid 类，可以创建自己的分面网格\n",
    "# 请查阅 seaborn 的文档（https://seaborn.pydata.org/）\n",
    "\n",
    "# 其它的 Python 可视化工具, 与其它开源库类似， Python创建图形的方式非常多\n",
    "# 利用工具如 Boken（https://bokeh.pydata.org/en/latest/） \n",
    "#        和 Plotly（https://github.com/plotly/plotly.py）\n",
    "#   现在可以创建动态交互图形，用于网页浏览器\n",
    "\n",
    "# 对于创建用于打印或网页的 静态图形，建议默认使用 matplotlib 和 附加的库， 比如 pandas 和 seaborn\n",
    "#  对于其它数据可视化要求，学习其它的可用工具可能是有用的\n",
    "\n",
    "# 熟悉一些基本的数据可视化操作，使用 pandas，matplotlib，seaborn\n",
    "# 如果视觉显示数据分析的结果对你的工作很重要，鼓励你寻求更多的资源来了解更高效的数据可视化\n",
    "# 这是一个活跃的研究领域，可以通过在线和纸质的形式学习许多优秀的资源"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
